code: def get_diabetes_data():
      """load (classification) data on diabetes"""
      data = loadarff(
          os.path.join(
              path_to_current_file, "../tests/test_data/diabetes.arff"
          )
      )
      data_np = np.array(
          list(map(lambda x: np.array(list(x)), data[0]))
      )
      X = data_np[:, :-1].astype("float32")
      y_text = data_np[:, -1].astype("str")
      y = (y_text == "tested_positive").astype(int)  # labels 0-1
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.75
      )  # split
      feature_names = [
          "#Pregnant",
          "Glucose concentration test",
          "Blood pressure(mmHg)",
          "Triceps skin fold thickness(mm)",
          "2-Hour serum insulin (mu U/ml)",
          "Body mass index",
          "Diabetes pedigree function",
          "Age (years)",
      ]
      return X_train, X_test, y_train, y_test, feature_names
  
file_path: imodels/notebooks/demo_helper.py
imports: import numpy
  from scipy.io.arff import loadarff
  from sklearn import metrics
  import matplotlib.pyplot
  from sklearn.datasets import fetch_openml
  from sklearn.model_selection import train_test_split
  import os
label_desc: Use image node_id 3 for calling a global function with example usage: get_diabetes_data() and returns: X_train,  X_test,  y_train,  y_test,  feature_names